THERA-IE: An AI-Enabled System for Therapeutic Indication Identification and Extraction from Biomedical Literature

Drug-indication knowledge underpins evidence-based prescribing but remains challenging to maintain manually. Existing resources incompletely represent off-label uses that may be captured within artificial intelligence models or described in the biomedical literature. This study presents THERA-IE (Therapeutic Hypothesis Extraction and Relationship Analytics - Indication Extraction), an open-source framework that combines natural language processing, transformer, and large language model (LLM) approaches to identify putative therapeutic indications. Twenty FDA-approved drugs were analyzed using SemMedDB, PubMedBERT, and three open-source LLMs (Llama 3.2, MedLlama 3, and Qwen 3). LLMs were implemented in both model-only ("Naïve") and literature-supported ("Literature-Based") modes. System performance was benchmarked against DrugBank. Naïve Llama 3.2 achieved the highest overall F1-score (0.60 ± 0.18), while the Literature-Based Llama 3.2 attained the highest precision (0.67 ± 0.29). SemMedDB achieved the highest recall (0.77 ± 0.25) but also produced the most false positives. THERA-IE identified an average of 4.8 ± 2.1 plausible off-label indications per drug. These findings demonstrate the potential of THERA-IE to enable scalable, reproducible extraction of therapeutic knowledge from biomedical literature.